In this paper a multi-layer recognizer is presented which is claimed to be device independent recognition system that can work with different gloves. The data from the gloves is converted into the posture(like is bend, is open ), temporal (Changes in the shape of the posture over time),gestural predicates(Motion of the postures). The predicates basically contain the information about the states of the fingers . Thus a 32 dimensional boolean vector is obtained through a feed forward neural network which is used to match with the Template.The template matcher works by computing the Euclidean distance between the observed predicate vector and every known template. The template that is found to be the shortest distance from the observed predicate vector is selected as the gesture with certain confidence value. Confidence values are basically weights which are used to weigh certain predicate so that the information from all predicates is equally utilized (as some predicates may give less information while others may give more).
They have tested their system on ASL (excluding letter J and Z) and have reported the accuracy in high 50's and mid Sixties by varying the sensors availabe in 22 sensor Cyber glove. They also reported that there was increase in accuracy when bigram context model was added to the recognition.
Discussion:
There is nothing great in the paper. using the predicates is an easy way but there is always scalability issue when users change. This method will not be suitable for multi-user environment. Even a single user would not be able to make this system to get higher accuracy because of intrinsic variability in the human gestures. That is why accuracy is very less. Also, using the less sensor data from similar glove does not make glove a different glove. As I understand different glove means altogether different sensors and architecture. I do not agree with their claim of device indpendence as they have projected in the paper.
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